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D. Çetin
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Automatic speech recognition (ASR) systems achieve very low word error rates on standard adult speech but underperform on populations whose speech deviates acoustically from the training distribution. Dysarthric speech, in which neurological injury impairs the muscular control of speech production, is among the most challenging cases: word error rates can exceed 90% on severely impaired speakers even after in-domain fine-tuning. Mixture-of-Experts (MoE) architectures, in which feed-forward sublayers are replaced by parallel “expert” sublayers and a small gating network routes each input to a subset of them, offer a candidate mechanism for inclusive ASR: in principle, the experts could come to specialize on different speaker populations and the router could allocate them where they are needed.
This thesis investigates whether MoE delivers that promise on dysarthric ASR, using the FastConformer-CTC Large encoder fine-tuned per fold on the TORGO and UASpeech corpora across 41 leave-one-speaker-out folds. Three research questions are addressed: how standard sparsely-gated MoE compares to a fine-tuned dense baseline (RQ1); what specialization the trained experts develop (RQ2); and whether either training-time supervision or inference-time router steering can push the model toward useful specialization (RQ3).
The answer to all three questions is negative. Five standard MoE configurations match but do not beat the fine-tuned dense baseline, and a parameter-equal follow-up shows that at equal feed-forward capacity, the MoE structure is actively worse than dense by up to 2.89 pp. The trained experts develop genuine functional specialization, but the axis of specialization is encoder depth, not speaker group or phonetic content: different layers prefer different expert pairs, and within any layer, routing is roughly content-independent and group-independent. Training-time supervision toward group-aware specialization collapses at inference, as the depth-based organization overrides the supervised partition. Inference-time router steering produces real per-speaker improvements under post-hoc selection of the bias strength, but neither of two deployment-realistic mechanisms for choosing that bias recovers the gain.
The MoE structure on this task does meaningful work, but not the work the task needs: it organizes the encoder’s computation along an axis the dense baseline already covers through its per-layer feed-forward weights. Routing-input variants, cross-architecture replication, and continual-learning extensions are flagged as the most direct follow-ups. ...
This thesis investigates whether MoE delivers that promise on dysarthric ASR, using the FastConformer-CTC Large encoder fine-tuned per fold on the TORGO and UASpeech corpora across 41 leave-one-speaker-out folds. Three research questions are addressed: how standard sparsely-gated MoE compares to a fine-tuned dense baseline (RQ1); what specialization the trained experts develop (RQ2); and whether either training-time supervision or inference-time router steering can push the model toward useful specialization (RQ3).
The answer to all three questions is negative. Five standard MoE configurations match but do not beat the fine-tuned dense baseline, and a parameter-equal follow-up shows that at equal feed-forward capacity, the MoE structure is actively worse than dense by up to 2.89 pp. The trained experts develop genuine functional specialization, but the axis of specialization is encoder depth, not speaker group or phonetic content: different layers prefer different expert pairs, and within any layer, routing is roughly content-independent and group-independent. Training-time supervision toward group-aware specialization collapses at inference, as the depth-based organization overrides the supervised partition. Inference-time router steering produces real per-speaker improvements under post-hoc selection of the bias strength, but neither of two deployment-realistic mechanisms for choosing that bias recovers the gain.
The MoE structure on this task does meaningful work, but not the work the task needs: it organizes the encoder’s computation along an axis the dense baseline already covers through its per-layer feed-forward weights. Routing-input variants, cross-architecture replication, and continual-learning extensions are flagged as the most direct follow-ups. ...
Automatic speech recognition (ASR) systems achieve very low word error rates on standard adult speech but underperform on populations whose speech deviates acoustically from the training distribution. Dysarthric speech, in which neurological injury impairs the muscular control of speech production, is among the most challenging cases: word error rates can exceed 90% on severely impaired speakers even after in-domain fine-tuning. Mixture-of-Experts (MoE) architectures, in which feed-forward sublayers are replaced by parallel “expert” sublayers and a small gating network routes each input to a subset of them, offer a candidate mechanism for inclusive ASR: in principle, the experts could come to specialize on different speaker populations and the router could allocate them where they are needed.
This thesis investigates whether MoE delivers that promise on dysarthric ASR, using the FastConformer-CTC Large encoder fine-tuned per fold on the TORGO and UASpeech corpora across 41 leave-one-speaker-out folds. Three research questions are addressed: how standard sparsely-gated MoE compares to a fine-tuned dense baseline (RQ1); what specialization the trained experts develop (RQ2); and whether either training-time supervision or inference-time router steering can push the model toward useful specialization (RQ3).
The answer to all three questions is negative. Five standard MoE configurations match but do not beat the fine-tuned dense baseline, and a parameter-equal follow-up shows that at equal feed-forward capacity, the MoE structure is actively worse than dense by up to 2.89 pp. The trained experts develop genuine functional specialization, but the axis of specialization is encoder depth, not speaker group or phonetic content: different layers prefer different expert pairs, and within any layer, routing is roughly content-independent and group-independent. Training-time supervision toward group-aware specialization collapses at inference, as the depth-based organization overrides the supervised partition. Inference-time router steering produces real per-speaker improvements under post-hoc selection of the bias strength, but neither of two deployment-realistic mechanisms for choosing that bias recovers the gain.
The MoE structure on this task does meaningful work, but not the work the task needs: it organizes the encoder’s computation along an axis the dense baseline already covers through its per-layer feed-forward weights. Routing-input variants, cross-architecture replication, and continual-learning extensions are flagged as the most direct follow-ups.
This thesis investigates whether MoE delivers that promise on dysarthric ASR, using the FastConformer-CTC Large encoder fine-tuned per fold on the TORGO and UASpeech corpora across 41 leave-one-speaker-out folds. Three research questions are addressed: how standard sparsely-gated MoE compares to a fine-tuned dense baseline (RQ1); what specialization the trained experts develop (RQ2); and whether either training-time supervision or inference-time router steering can push the model toward useful specialization (RQ3).
The answer to all three questions is negative. Five standard MoE configurations match but do not beat the fine-tuned dense baseline, and a parameter-equal follow-up shows that at equal feed-forward capacity, the MoE structure is actively worse than dense by up to 2.89 pp. The trained experts develop genuine functional specialization, but the axis of specialization is encoder depth, not speaker group or phonetic content: different layers prefer different expert pairs, and within any layer, routing is roughly content-independent and group-independent. Training-time supervision toward group-aware specialization collapses at inference, as the depth-based organization overrides the supervised partition. Inference-time router steering produces real per-speaker improvements under post-hoc selection of the bias strength, but neither of two deployment-realistic mechanisms for choosing that bias recovers the gain.
The MoE structure on this task does meaningful work, but not the work the task needs: it organizes the encoder’s computation along an axis the dense baseline already covers through its per-layer feed-forward weights. Routing-input variants, cross-architecture replication, and continual-learning extensions are flagged as the most direct follow-ups.